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Segmentation of fingerprint images based on bi-level combination of global and local processing
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0001-6526-6537
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2012 (English)In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 2, 97-120 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.

Place, publisher, year, edition, pages
Berlin: Walter de Gruyter, 2012. Vol. 21, no 2, 97-120 p.
Keyword [en]
Average errors; False negatives; False positive; Features; Fingerprint images; Fingerprint reader; Fingerprints; Global levels; Global thresholding; High robustness; Human expert; Local processing; Low qualities; Manual examination; Post processing; Pre-processing, Edge detection; Fuzzy sets, Image segmentation
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-10163DOI: 10.1515/jisys-2012-0005OAI: oai:DiVA.org:du-10163DiVA: diva2:532728
Available from: 2012-06-12 Created: 2012-06-12 Last updated: 2016-02-12Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf